"Finding"的相关文档

标签“Finding”的相关文档，共12条

Local Algorithms for Finding Densely Connected Clusters
LocalAlgorithmsforFindingDenselyConnectedClustersPeterMacgregor1HeSun1Abstract1.1.OurContributionLocalgraphclusteringisanimportantalgorithmicWestudylocalgraphclusteringforlearningthestructuretechni...
for Algorithms Local Connected Finding
2023-11-16 19:05:0913691.48 MB3
下载文档
Finding the Stochastic Shortest Path with Low Regret the Adversarial Cost and Unknown Transition Case
FindingtheStochasticShortestPathwithLowRegret:TheAdversarialCostandUnknownTransitionCaseLiyuChen1HaipengLuo1Abstractendwithinaﬁxednumberofstepsisextensivelystudiedinrecentyears(oftenknownasepisodi...
with Stochastic the low Path
2023-11-16 18:38:12797422.01 KB6
下载文档
Finding Relevant Information via a Discrete Fourier Expansion
FindingRelevantInformationviaaDiscreteFourierExpansionMohsenHeidari1JithinK.Sreedharan2GilI.Shamir3WojciechSzpankowski1Abstractcapturenon-linearrelations(Grettonetal.,2005;Chenetal.,2017;Weietal.,2...
via Discrete Information Fourier Finding
2023-11-16 18:38:111645811 KB1
下载文档
Finding k in Latent $k-$ polytope
FindingkinLatentk-PolytopeChiranjibBhattacharyya1RaviKannan2AmitKumar3Abstract(Bhattacharyya&Kannan,2020)suggestedLatentkpolytope(LkP),anunifyingmodelwhichincludesallofTherecentlyintroducedLatentk...
in Latent Finding Polytope
2023-11-16 18:38:111559347.74 KB12
下载文档
Efficient Lottery Ticket Finding Less Data is More
EfﬁcientLotteryTicketFinding:LessDataisMoreZhenyuZhang1XuxiChen2TianlongChen2ZhangyangWang293CIFAR-1072CIFAR-1007045000Abstract924500024457684082491Thelotterytickethypothesis(LTH)(Frankle&90171203...
Efficient Data is Finding less
2023-11-16 18:37:5819194.42 MB27
下载文档
The Complexity of Finding Stationary Points with Stochastic Gradient Descent
TheComplexityofFindingStationaryPointswithStochasticGradientDescentYoelDrori1OhadShamir12Abstractisnottominimizef(x)overx,butrather∇f(x).Thisquestionofﬁndingstationarypointshasgainedmoreatten-Wes...
of with Complexity the Points
2023-11-14 21:46:42706368.62 KB14
下载文档
Sparsified Linear Programming for Zero-Sum Equilibrium Finding
SparsiﬁedLinearProgrammingforZero-SumEquilibriumFindingBrianHuZhang1TuomasSandholm1234Abstractgramming(LP)canbeusedtosolve—thatis,toﬁndaNashequilibriumin—imperfect-informationtwo-playerzero-sum...
for Equilibrium Linear Sparsified Programming
2023-11-14 21:46:28922458.91 KB17
下载文档
Finding trainable sparse networks through Neural Tangent Transfer
FindingtrainablesparsenetworksthroughNeuralTangentTransferTianlinLiu12FriedemannZenke1Abstract2017).Tothisdate,mostofthestudiesonsparseartiﬁcialneuralnetworkshavefocusedonposthocpruning,wherebyDee...
Neural Networks Sparse through Finding
2023-11-14 21:44:141009717.44 KB30
下载文档
Complexity of Finding Stationary Points of Nonconvex Nonsmooth Functions
ComplexityofFindingStationaryPointsofNonsmoothNonconvexFunctionsJingzhaoZhang1HongzhouLin1StefanieJegelka1SuvritSra1AliJadbabaie1AbstractTable1.Whentheproblemisnonconvexandnonsmooth,ﬁnd-inga-stati...
of Complexity Points Nonconvex Nonsmooth
2023-11-14 21:43:30796722.36 KB12
下载文档
Finding Options that Minimize Planning Time
FindingOptionsthatMinimizePlanningTimeYuuJinnai1DavidAbel1DEllisHershkowitz2MichaelL.Littman1GeorgeKonidaris1AbstractBarto,2009;Bacon,2013;Moradietal.,2012),ﬁndingre-peatedpolicyfragments(Pickett&...
Time that Planning Finding minimize
2023-11-13 14:47:1212831.94 MB11
下载文档
Finding Mixed Nash Equilibria of Generative Adversarial Networks
FindingMixedNashEquilibriaofGenerativeAdversarialNetworksYa-PingHsieh1ChenLiu1VolkanCevher1Abstract2016).Indeed,thestochasticgradientdescent(SGD),alongwithitsvariants,remainthemethodofchoicefortrai...
of Adversarial Generative Nash Mixed
2023-11-13 14:47:128413.01 MB20
下载文档
Finding Influential Training Samples for Gradient Boosted Decision Trees
FindingInﬂuentialTrainingSamplesforGradientBoostedDecisionTreesBorisSharchilev12YuryUstinovsky3PavelSerdyukov2MaartendeRijke1Abstract(2)boostingdeveloper’strustinthemodel’sperformanceinscenarios...
for Gradient Boosted Samples Training
2023-11-13 11:59:36606417.17 KB1
下载文档

首页上页 1 下页尾页